MAPS

Load in Census Shapefiles from GitHub

Note: To see how these shapefiles were created, check Shapefiles to Geojson Files HTML file for more information.

file.url <- "https://raw.githubusercontent.com/lecy/analyzing-nonprofit-service-areas/master/SHAPEFILES/syr_merged.geojson"

syr <- geojson_read( file.url, method="local", what="sp")

spTransform(syr, CRS("+proj=longlat +datum=WGS84"))
## class       : SpatialPolygonsDataFrame 
## features    : 186 
## extent      : -76.64471, -75.23954, 42.72384, 43.70702  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
## variables   : 68
## names       : STATEFP.1, COUNTYFP.1, TRACTCE.1,     GEOID.1, NAME.1,          NAMELSAD.1, MTFCC.1, FUNCSTAT.1,   ALAND.1, AWATER.1,  INTPTLAT.1,   INTPTLON.1, STATEFP.2, COUNTYFP.2, TRACTCE.2, ... 
## min values  :        36,        053,    030101, 36053030101, 301.01, Census Tract 301.01,   G5020,          S, 102677781,        0, +42.7960459, -075.3432945,        36,        067,    000100, ... 
## max values  :        36,        053,    031100, 36053031100,    311,    Census Tract 311,   G5020,          S,   8864612,    93115, +43.1261230, -075.8928470,        36,        067,    940000, ...
file.url <- "https://raw.githubusercontent.com/lecy/analyzing-nonprofit-service-areas/master/SHAPEFILES/syr_merged_cen.geojson"

centroids <- geojson_read( file.url, method="local", what="sp")

spTransform(centroids, CRS("+proj=longlat +datum=WGS84"))
## class       : SpatialPointsDataFrame 
## features    : 186 
## extent      : -76.55702, -75.34358, 42.78044, 43.63666  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
## variables   : 1
## names       : dummy 
## min values  :     0 
## max values  :     0
file.url <- "https://raw.githubusercontent.com/lecy/analyzing-nonprofit-service-areas/master/SHAPEFILES/pov_orgs.geojson"

pov_orgs <- geojson_read( file.url, method="local", what="sp")

spTransform(pov_orgs, CRS("+proj=longlat +datum=WGS84"))
## class       : SpatialPointsDataFrame 
## features    : 1095 
## extent      : -76.58932, -75.27822, 42.7584, 43.58445  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
## variables   : 21
## names       :                                 whole_address,       EIN,  FIPS, NTEECC, FILER, ZFILER,                               NAME,                  ADDRESS,          CITY, STATE,  ZIP5,  GEN, SUBSECCD, RULEDATE, FNDNCD, ... 
## min values  :      1 ARKIE ALBANESE AVE, MANLIUS, NY, 13104, 010587946, 36053,    B61,     N,      N, 0SWEGO COUNTY DEPUTIES ASSOCIATION,     1 ARKIE ALBANESE AVE, BALDWINSVILLE,    NY, 13027, 0000,       02,   000000,     00, ... 
## max values  : SUNY COLLEGE OF FORESTRY, SYRACUSE, NY, 13210, 952505709, 36075,    T99,     Y,      Y,       ZONTA INTERNATIONAL SYRACUSE, SUNY COLLEGE OF FORESTRY, WEST EDMESTON,    NY, 13485, 9434,       92,   201607,     21, ...
par( mar=c(0,0,0,0) )  # drop plot margins

Check shapefiles with plots

plot( syr )
plot( centroids, col="black", pch = 20, add = T)

plot( syr )
plot( centroids, col="black", pch = 20, add = T )
plot( pov_orgs, col="red", lwd = 2, add = T )

names(syr)
##  [1] "STATEFP.1"    "COUNTYFP.1"   "TRACTCE.1"    "GEOID.1"     
##  [5] "NAME.1"       "NAMELSAD.1"   "MTFCC.1"      "FUNCSTAT.1"  
##  [9] "ALAND.1"      "AWATER.1"     "INTPTLAT.1"   "INTPTLON.1"  
## [13] "STATEFP.2"    "COUNTYFP.2"   "TRACTCE.2"    "GEOID.2"     
## [17] "NAME.2"       "NAMELSAD.2"   "MTFCC.2"      "FUNCSTAT.2"  
## [21] "ALAND.2"      "AWATER.2"     "INTPTLAT.2"   "INTPTLON.2"  
## [25] "STATEFP"      "COUNTYFP"     "TRACTCE"      "GEOID"       
## [29] "NAME"         "NAMELSAD"     "MTFCC"        "FUNCSTAT"    
## [33] "ALAND"        "AWATER"       "INTPTLAT"     "INTPTLON"    
## [37] "GEOID_full"   "NAME.3"       "GEOID.3"      "state"       
## [41] "county"       "tract"        "mdn_hous_val" "tenure_tot"  
## [45] "tenure_own"   "tenure_rent"  "tot_occup"    "occupied"    
## [49] "vacant"       "tot_pop"      "mhh_income"   "poverty"     
## [53] "labor_part"   "unemploy"     "high_sch"     "ged"         
## [57] "tot_race"     "white"        "black"        "am_ind"      
## [61] "asian"        "islander"     "other"        "mixed"       
## [65] "not_hispanic" "hispanic"     "porp_white"   "porp_m"

Create zoom enabled map of Syracuse, NY

map <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-76.1474244, lat=43.0481221, popup="Downtown Syracuse, NY")

map %>% addProviderTiles(providers$CartoDB)

Set the view of the Syracuse MSA

syr_map <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addTiles() %>% setView(-76.1474244, 43.0481221, zoom = 9) %>%
  addMarkers(lng=-76.1474244, lat=43.0481221, popup="Downtown Syracuse, NY") %>%
  addPolygons(data = syr, fillOpacity = 0.3) %>%
  addCircles(data = pov_orgs) %>%
  addCircles(data = centroids)

syr_map %>% addProviderTiles(providers$CartoDB)

Edit color format of polygons and data points

syr_map <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addTiles() %>% setView(-76.1474244, 43.0481221, zoom = 9) %>%
  addMarkers(lng=-76.1474244, lat=43.0481221, popup="Downtown Syracuse, NY") %>%
  addPolygons(data = syr, fillOpacity = 0.3, weight = 2) %>%
  addCircles(data = pov_orgs, color= "red", opacity = 1.0) %>%
  addCircles(data = centroids, color= "black", opacity = 1.0)

syr_map %>% addProviderTiles(providers$CartoDB)

Adding census tract information to map

# change variables to be numeric

syr$porp_white <- as.numeric(as.character(syr$porp_white))
syr$porp_m <- as.numeric(as.character(syr$porp_m))
syr$mhh_income <- as.numeric(as.character(syr$mhh_income))
class(syr$porp_m)
## [1] "numeric"
class(syr$porp_white)
## [1] "numeric"
class(syr$mhh_income)
## [1] "numeric"
# By race

pal <- colorNumeric(
  palette = "Blues",
  domain = syr$porp_m )


syr_map <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addTiles() %>% setView(-76.1474244, 43.0481221, zoom = 9) %>%
  addMarkers(lng=-76.1474244, lat=43.0481221, popup="Downtown Syracuse, NY") %>%
  addPolygons(data = syr, fillOpacity = 0.3, weight = 2,
              color = ~pal(syr$porp_m)) %>%
  addCircles(data = pov_orgs, color= "red", opacity = 1.0) %>%
  addCircles(data = centroids, color= "black", opacity = 1.0) 


syr_map %>% addProviderTiles(providers$CartoDB)
# By Income level

pal2 <- colorNumeric(
  palette = "Blues",
  domain = syr$mhh_income )


syr_map2 <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addTiles() %>% setView(-76.1474244, 43.0481221, zoom = 9) %>%
  addMarkers(lng=-76.1474244, lat=43.0481221, popup="Downtown Syracuse, NY") %>%
  addPolygons(data = syr, fillOpacity = 0.3, weight = 2,
              color = ~pal2(syr$mhh_income)) %>%
  addCircles(data = pov_orgs, color= "red", opacity = 1.0) %>%
  addCircles(data = centroids, color= "black", opacity = 1.0) 


syr_map2 %>% addProviderTiles(providers$CartoDB)

ANALYSIS

Create Service Catchment Areas

# Create buffer, calculate number of nonprofit points around centroids

centroids_buff <- gBuffer(spgeom = centroids, width = .024, joinStyle="ROUND",                             byid = T)

spTransform(centroids_buff, CRS("+proj=longlat +datum=WGS84"))

# join census data to centroid buffer
centroids_buff$ID <- c(1:186)
syr$ID <- c(1:186)


test.xy <- pov_orgs@data[c("lon", "lat")]
coordinates( test.xy ) = ~lon+lat

CRS.new<-CRS("+proj=longlat +datum=WGS84")
proj4string(centroids_buff) <- CRS.new 
proj4string(pov_orgs) <- CRS.new

over <- over(pov_orgs,  centroids_buff)

Table(over$tract)
save table as new var in syr


# other option?
# syr$test <- 1:nrow(over_clean)

plot(centroids_buff)
points( pov_orgs, col="darkorange4", pch= 1 )


pal2 <- colorNumeric(
  palette = "Blues",
  domain = syr$mhh_income )

syr_map2 <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addTiles() %>% setView(-76.1474244, 43.0481221, zoom = 9) %>%
  addMarkers(lng=-76.1474244, lat=43.0481221, popup="Downtown Syracuse, NY") %>%
  addPolygons(data = syr, fillOpacity = 0.3, weight = 2,
              color = ~pal2(syr$mhh_income)) %>%
  addCircles(data = pov_orgs, color= "red", opacity = 1.0) %>%
  addCircles(data = centroids, color= "black", opacity = 1.0) %>%
  addPolygons(data = centroids_buff, fillOpacity = 0.3, weight = 2)
             
syr_map2 %>% addProviderTiles(providers$CartoDB)

# Not quite sure what is needed. How do I add up values for every centroid from the overlay?

# Similar to above, but combines over and removing NAs into one step
# pov_orgs_t <- pov_orgs 
# pov_orgs_t$test <- pov_orgs[centroids_buff,]

Analysis continued

# Poportion of census tract that are high/low diversity
  # number of nonprofits by type in high/low racially diverse areas
  # number of nonprofits by type in low/high income census tracts


# Corr analysis between nonprofit access and census tract attributes
  # Number of nonprofits location by census tract income or race
    # Race
    # Income

Final section: Account for all urban census tracts in 25 cities